Overview

Dataset statistics

Number of variables29
Number of observations1385
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory313.9 KiB
Average record size in memory232.1 B

Variable types

Numeric20
Categorical9

Alerts

RNA 12 is highly correlated with RNA EOT and 1 other fieldsHigh correlation
RNA EOT is highly correlated with RNA 12 and 1 other fieldsHigh correlation
RNA EF is highly correlated with RNA 12 and 1 other fieldsHigh correlation
ALT 36 is highly correlated with ALT 48 and 1 other fieldsHigh correlation
ALT 48 is highly correlated with ALT 36 and 1 other fieldsHigh correlation
ALT after 24 w is highly correlated with ALT 36 and 1 other fieldsHigh correlation
RNA EOT is highly correlated with RNA EFHigh correlation
RNA EF is highly correlated with RNA EOTHigh correlation

Reproduction

Analysis started2022-02-01 20:21:59.858811
Analysis finished2022-02-01 20:23:11.155454
Duration1 minute and 11.3 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

Distinct30
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.31913357
Minimum32
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:11.301644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile33
Q139
median46
Q354
95-th percentile60
Maximum61
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.781505747
Coefficient of variation (CV)0.1895870037
Kurtosis-1.253372549
Mean46.31913357
Median Absolute Deviation (MAD)8
Skewness0.006834851623
Sum64152
Variance77.11484318
MonotonicityNot monotonic
2022-02-01T17:23:11.487037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5658
 
4.2%
3356
 
4.0%
3954
 
3.9%
4751
 
3.7%
3651
 
3.7%
4351
 
3.7%
3250
 
3.6%
5349
 
3.5%
3848
 
3.5%
4148
 
3.5%
Other values (20)869
62.7%
ValueCountFrequency (%)
3250
3.6%
3356
4.0%
3448
3.5%
3544
3.2%
3651
3.7%
3746
3.3%
3848
3.5%
3954
3.9%
4037
2.7%
4148
3.5%
ValueCountFrequency (%)
6140
2.9%
6045
3.2%
5948
3.5%
5847
3.4%
5746
3.3%
5658
4.2%
5544
3.2%
5446
3.3%
5349
3.5%
5245
3.2%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
1
707 
2
678 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1707
51.0%
2678
49.0%

Length

2022-02-01T17:23:11.649041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:11.726431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1707
51.0%
2678
49.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BMI
Real number (ℝ≥0)

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.60866426
Minimum22
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:11.795452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q125
median29
Q332
95-th percentile35
Maximum35
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.076215379
Coefficient of variation (CV)0.1424818489
Kurtosis-1.244280222
Mean28.60866426
Median Absolute Deviation (MAD)4
Skewness-0.03557283505
Sum39623
Variance16.61553181
MonotonicityNot monotonic
2022-02-01T17:23:11.945709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
34118
 
8.5%
24112
 
8.1%
28107
 
7.7%
31104
 
7.5%
33104
 
7.5%
35100
 
7.2%
2799
 
7.1%
2397
 
7.0%
2296
 
6.9%
3095
 
6.9%
Other values (4)353
25.5%
ValueCountFrequency (%)
2296
6.9%
2397
7.0%
24112
8.1%
2592
6.6%
2678
5.6%
2799
7.1%
28107
7.7%
2993
6.7%
3095
6.9%
31104
7.5%
ValueCountFrequency (%)
35100
7.2%
34118
8.5%
33104
7.5%
3290
6.5%
31104
7.5%
3095
6.9%
2993
6.7%
28107
7.7%
2799
7.1%
2678
5.6%

Fever
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2
714 
1
671 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2714
51.6%
1671
48.4%

Length

2022-02-01T17:23:12.111705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:12.203705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2714
51.6%
1671
48.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Nausea/Vomting
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2
696 
1
689 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2696
50.3%
1689
49.7%

Length

2022-02-01T17:23:12.272847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:12.347141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2696
50.3%
1689
49.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Headache
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
1
698 
2
687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1698
50.4%
2687
49.6%

Length

2022-02-01T17:23:12.428301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:12.497314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1698
50.4%
2687
49.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Diarrhea
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2
696 
1
689 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2696
50.3%
1689
49.7%

Length

2022-02-01T17:23:12.591037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:12.655479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2696
50.3%
1689
49.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
1
694 
2
691 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1694
50.1%
2691
49.9%

Length

2022-02-01T17:23:12.738887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:12.811657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1694
50.1%
2691
49.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Jaundice
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2
694 
1
691 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2694
50.1%
1691
49.9%

Length

2022-02-01T17:23:13.047229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:13.133130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2694
50.1%
1691
49.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Epigastric pain
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2
698 
1
687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2698
50.4%
1687
49.6%

Length

2022-02-01T17:23:13.207019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:13.273064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2698
50.4%
1687
49.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WBC
Real number (ℝ≥0)

Distinct1305
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7533.386282
Minimum2991
Maximum12101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:13.368399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2991
5-th percentile3414
Q15219
median7498
Q39902
95-th percentile11676
Maximum12101
Range9110
Interquartile range (IQR)4683

Descriptive statistics

Standard deviation2668.220333
Coefficient of variation (CV)0.3541860503
Kurtosis-1.220602077
Mean7533.386282
Median Absolute Deviation (MAD)2336
Skewness0.0160808651
Sum10433740
Variance7119399.744
MonotonicityNot monotonic
2022-02-01T17:23:13.526610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115303
 
0.2%
32713
 
0.2%
60383
 
0.2%
40823
 
0.2%
34143
 
0.2%
67232
 
0.1%
101042
 
0.1%
116482
 
0.1%
45462
 
0.1%
88692
 
0.1%
Other values (1295)1360
98.2%
ValueCountFrequency (%)
29911
0.1%
29951
0.1%
30011
0.1%
30071
0.1%
30091
0.1%
30101
0.1%
30201
0.1%
30291
0.1%
30431
0.1%
30501
0.1%
ValueCountFrequency (%)
121011
0.1%
120941
0.1%
120931
0.1%
120881
0.1%
120821
0.1%
120781
0.1%
120671
0.1%
120641
0.1%
120611
0.1%
120491
0.1%

RBC
Real number (ℝ≥0)

Distinct1384
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4422129.611
Minimum3816422
Maximum5018451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:13.690951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3816422
5-th percentile3879629.2
Q14121374
median4438465
Q34721279
95-th percentile4958421
Maximum5018451
Range1202029
Interquartile range (IQR)599905

Descriptive statistics

Standard deviation346357.7116
Coefficient of variation (CV)0.07832373587
Kurtosis-1.19420021
Mean4422129.611
Median Absolute Deviation (MAD)301507
Skewness-0.04766267071
Sum6124649511
Variance1.199636644 × 1011
MonotonicityNot monotonic
2022-02-01T17:23:13.842807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41533692
 
0.1%
39126591
 
0.1%
38427921
 
0.1%
45687421
 
0.1%
45557491
 
0.1%
41431651
 
0.1%
47959591
 
0.1%
46294581
 
0.1%
40122521
 
0.1%
46277401
 
0.1%
Other values (1374)1374
99.2%
ValueCountFrequency (%)
38164221
0.1%
38169971
0.1%
38178331
0.1%
38183131
0.1%
38184041
0.1%
38198781
0.1%
38200241
0.1%
38208641
0.1%
38212311
0.1%
38214441
0.1%
ValueCountFrequency (%)
50184511
0.1%
50180341
0.1%
50175911
0.1%
50159121
0.1%
50150301
0.1%
50148901
0.1%
50141691
0.1%
50136811
0.1%
50129141
0.1%
50113881
0.1%

HGB
Real number (ℝ≥0)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.58772563
Minimum10
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:13.974068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q111
median13
Q314
95-th percentile15
Maximum15
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.71351128
Coefficient of variation (CV)0.1361255663
Kurtosis-1.271331631
Mean12.58772563
Median Absolute Deviation (MAD)2
Skewness-0.04688702911
Sum17434
Variance2.936120907
MonotonicityNot monotonic
2022-02-01T17:23:14.090010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
15258
18.6%
12236
17.0%
14231
16.7%
13225
16.2%
11223
16.1%
10212
15.3%
ValueCountFrequency (%)
10212
15.3%
11223
16.1%
12236
17.0%
13225
16.2%
14231
16.7%
15258
18.6%
ValueCountFrequency (%)
15258
18.6%
14231
16.7%
13225
16.2%
12236
17.0%
11223
16.1%
10212
15.3%

Plat
Real number (ℝ≥0)

Distinct1375
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158348.0606
Minimum93013
Maximum226464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:14.345165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum93013
5-th percentile98761.4
Q1124479
median157916
Q3190314
95-th percentile219865.2
Maximum226464
Range133451
Interquartile range (IQR)65835

Descriptive statistics

Standard deviation38794.78555
Coefficient of variation (CV)0.2449969099
Kurtosis-1.20412637
Mean158348.0606
Median Absolute Deviation (MAD)32848
Skewness0.0324416118
Sum219312064
Variance1505035386
MonotonicityNot monotonic
2022-02-01T17:23:14.602020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1670312
 
0.1%
2185542
 
0.1%
1033682
 
0.1%
1042722
 
0.1%
1713782
 
0.1%
2231372
 
0.1%
1285692
 
0.1%
1631452
 
0.1%
1993072
 
0.1%
1868502
 
0.1%
Other values (1365)1365
98.6%
ValueCountFrequency (%)
930131
0.1%
930341
0.1%
932421
0.1%
933011
0.1%
936031
0.1%
937081
0.1%
937311
0.1%
937551
0.1%
937851
0.1%
937861
0.1%
ValueCountFrequency (%)
2264641
0.1%
2264281
0.1%
2262591
0.1%
2261291
0.1%
2260711
0.1%
2259411
0.1%
2258181
0.1%
2258161
0.1%
2257231
0.1%
2255591
0.1%

AST 1
Real number (ℝ≥0)

Distinct90
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.77472924
Minimum39
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:14.885184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile43
Q160
median83
Q3105
95-th percentile123.8
Maximum128
Range89
Interquartile range (IQR)45

Descriptive statistics

Standard deviation25.99324217
Coefficient of variation (CV)0.3140238863
Kurtosis-1.204960281
Mean82.77472924
Median Absolute Deviation (MAD)23
Skewness0.03013154245
Sum114643
Variance675.6486384
MonotonicityNot monotonic
2022-02-01T17:23:15.163189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8626
 
1.9%
12422
 
1.6%
12122
 
1.6%
6721
 
1.5%
6621
 
1.5%
9321
 
1.5%
11021
 
1.5%
5520
 
1.4%
6220
 
1.4%
12819
 
1.4%
Other values (80)1172
84.6%
ValueCountFrequency (%)
3916
1.2%
4016
1.2%
4118
1.3%
4219
1.4%
4316
1.2%
4417
1.2%
4517
1.2%
4614
1.0%
4712
0.9%
4813
0.9%
ValueCountFrequency (%)
12819
1.4%
12715
1.1%
1268
 
0.6%
1256
 
0.4%
12422
1.6%
12313
0.9%
12215
1.1%
12122
1.6%
12014
1.0%
11917
1.2%

ALT 1
Real number (ℝ≥0)

Distinct90
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.91624549
Minimum39
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:15.378630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile43
Q162
median83
Q3106
95-th percentile125
Maximum128
Range89
Interquartile range (IQR)44

Descriptive statistics

Standard deviation25.92279952
Coefficient of variation (CV)0.3089127662
Kurtosis-1.164678686
Mean83.91624549
Median Absolute Deviation (MAD)22
Skewness0.01792627375
Sum116224
Variance671.991535
MonotonicityNot monotonic
2022-02-01T17:23:15.574330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12630
 
2.2%
10925
 
1.8%
8624
 
1.7%
7822
 
1.6%
6322
 
1.6%
9322
 
1.6%
10122
 
1.6%
10221
 
1.5%
7021
 
1.5%
3920
 
1.4%
Other values (80)1156
83.5%
ValueCountFrequency (%)
3920
1.4%
4012
0.9%
4118
1.3%
4213
0.9%
4314
1.0%
4414
1.0%
4512
0.9%
4610
0.7%
4711
0.8%
4817
1.2%
ValueCountFrequency (%)
12817
1.2%
12717
1.2%
12630
2.2%
12511
 
0.8%
12419
1.4%
12315
1.1%
12217
1.2%
12118
1.3%
12012
 
0.9%
11912
 
0.9%

ALT4
Real number (ℝ≥0)

Distinct90
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.40577617
Minimum39
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:15.786331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile43
Q161
median82
Q3107
95-th percentile124
Maximum128
Range89
Interquartile range (IQR)46

Descriptive statistics

Standard deviation26.52972967
Coefficient of variation (CV)0.3180802444
Kurtosis-1.25148903
Mean83.40577617
Median Absolute Deviation (MAD)23
Skewness0.02328852466
Sum115517
Variance703.8265562
MonotonicityNot monotonic
2022-02-01T17:23:16.177583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12027
 
1.9%
6426
 
1.9%
7125
 
1.8%
4324
 
1.7%
11822
 
1.6%
11421
 
1.5%
6721
 
1.5%
11721
 
1.5%
8121
 
1.5%
5320
 
1.4%
Other values (80)1157
83.5%
ValueCountFrequency (%)
3915
1.1%
4017
1.2%
4118
1.3%
4213
0.9%
4324
1.7%
4417
1.2%
4518
1.3%
4613
0.9%
4712
0.9%
4816
1.2%
ValueCountFrequency (%)
12819
1.4%
12718
1.3%
1269
 
0.6%
12514
1.0%
12418
1.3%
12315
1.1%
12214
1.0%
12113
0.9%
12027
1.9%
11916
1.2%

ALT 12
Real number (ℝ≥0)

Distinct90
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.51046931
Minimum39
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:16.421037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile43
Q160
median84
Q3106
95-th percentile123
Maximum128
Range89
Interquartile range (IQR)46

Descriptive statistics

Standard deviation26.06447788
Coefficient of variation (CV)0.312110303
Kurtosis-1.200700237
Mean83.51046931
Median Absolute Deviation (MAD)23
Skewness-0.01116728514
Sum115662
Variance679.3570074
MonotonicityNot monotonic
2022-02-01T17:23:16.610997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3922
 
1.6%
8922
 
1.6%
6822
 
1.6%
10322
 
1.6%
11621
 
1.5%
12121
 
1.5%
8521
 
1.5%
6021
 
1.5%
4320
 
1.4%
5920
 
1.4%
Other values (80)1173
84.7%
ValueCountFrequency (%)
3922
1.6%
4012
0.9%
4111
0.8%
4218
1.3%
4320
1.4%
4415
1.1%
4512
0.9%
4613
0.9%
4719
1.4%
4816
1.2%
ValueCountFrequency (%)
12816
1.2%
12717
1.2%
12613
0.9%
1257
 
0.5%
12416
1.2%
12313
0.9%
12217
1.2%
12121
1.5%
12016
1.2%
11916
1.2%

ALT 24
Real number (ℝ≥0)

Distinct90
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.70902527
Minimum39
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:16.809264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile42
Q161
median83
Q3107
95-th percentile124
Maximum128
Range89
Interquartile range (IQR)46

Descriptive statistics

Standard deviation26.20599445
Coefficient of variation (CV)0.3130605615
Kurtosis-1.211084312
Mean83.70902527
Median Absolute Deviation (MAD)23
Skewness-0.03309517243
Sum115937
Variance686.7541454
MonotonicityNot monotonic
2022-02-01T17:23:17.051630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11527
 
1.9%
10125
 
1.8%
12624
 
1.7%
8422
 
1.6%
10422
 
1.6%
12222
 
1.6%
10722
 
1.6%
4122
 
1.6%
8121
 
1.5%
4721
 
1.5%
Other values (80)1157
83.5%
ValueCountFrequency (%)
3916
1.2%
4017
1.2%
4122
1.6%
4218
1.3%
4320
1.4%
4414
1.0%
459
0.6%
4616
1.2%
4721
1.5%
487
 
0.5%
ValueCountFrequency (%)
12810
0.7%
12716
1.2%
12624
1.7%
12512
0.9%
12413
0.9%
12313
0.9%
12222
1.6%
1217
 
0.5%
12019
1.4%
11914
1.0%

ALT 36
Real number (ℝ≥0)

HIGH CORRELATION

Distinct91
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.11768953
Minimum5
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:17.266985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile43
Q161
median84
Q3106
95-th percentile124
Maximum128
Range123
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.3990308
Coefficient of variation (CV)0.3176102578
Kurtosis-1.11078595
Mean83.11768953
Median Absolute Deviation (MAD)22
Skewness-0.04143070854
Sum115118
Variance696.908827
MonotonicityNot monotonic
2022-02-01T17:23:17.546990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6425
 
1.8%
11424
 
1.7%
3924
 
1.7%
5123
 
1.7%
9523
 
1.7%
8722
 
1.6%
6521
 
1.5%
4621
 
1.5%
12820
 
1.4%
5720
 
1.4%
Other values (81)1162
83.9%
ValueCountFrequency (%)
53
 
0.2%
3924
1.7%
4011
0.8%
4119
1.4%
4211
0.8%
4318
1.3%
4411
0.8%
4516
1.2%
4621
1.5%
4716
1.2%
ValueCountFrequency (%)
12820
1.4%
12714
1.0%
12613
0.9%
12520
1.4%
1249
0.6%
12313
0.9%
12216
1.2%
12115
1.1%
12015
1.1%
11919
1.4%

ALT 48
Real number (ℝ≥0)

HIGH CORRELATION

Distinct91
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.62960289
Minimum5
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:17.754166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile43
Q161
median83
Q3106
95-th percentile124
Maximum128
Range123
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.22395469
Coefficient of variation (CV)0.3135726319
Kurtosis-1.076860808
Mean83.62960289
Median Absolute Deviation (MAD)22
Skewness-0.04445700614
Sum115827
Variance687.6957993
MonotonicityNot monotonic
2022-02-01T17:23:17.961154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7825
 
1.8%
10124
 
1.7%
12024
 
1.7%
5623
 
1.7%
4423
 
1.7%
12323
 
1.7%
10723
 
1.7%
7222
 
1.6%
11421
 
1.5%
12820
 
1.4%
Other values (81)1157
83.5%
ValueCountFrequency (%)
53
 
0.2%
3915
1.1%
4011
0.8%
4115
1.1%
4215
1.1%
4317
1.2%
4423
1.7%
4513
0.9%
4611
0.8%
4716
1.2%
ValueCountFrequency (%)
12820
1.4%
12711
0.8%
12618
1.3%
12517
1.2%
12418
1.3%
12323
1.7%
1229
 
0.6%
12114
1.0%
12024
1.7%
11915
1.1%

ALT after 24 w
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.43826715
Minimum5
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:18.166165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile23
Q128
median34
Q340
95-th percentile44
Maximum45
Range40
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.073569416
Coefficient of variation (CV)0.2115411479
Kurtosis-0.7491137993
Mean33.43826715
Median Absolute Deviation (MAD)6
Skewness-0.111222441
Sum46312
Variance50.03538428
MonotonicityNot monotonic
2022-02-01T17:23:18.367148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3472
 
5.2%
2569
 
5.0%
3068
 
4.9%
4565
 
4.7%
2465
 
4.7%
4364
 
4.6%
3662
 
4.5%
3360
 
4.3%
2860
 
4.3%
4159
 
4.3%
Other values (15)741
53.5%
ValueCountFrequency (%)
53
 
0.2%
2257
4.1%
2357
4.1%
2465
4.7%
2569
5.0%
2649
3.5%
2741
3.0%
2860
4.3%
2951
3.7%
3068
4.9%
ValueCountFrequency (%)
4565
4.7%
4450
3.6%
4364
4.6%
4256
4.0%
4159
4.3%
4056
4.0%
3957
4.1%
3849
3.5%
3751
3.7%
3662
4.5%

RNA Base
Real number (ℝ≥0)

Distinct1384
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean590951.2188
Minimum11
Maximum1201086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:18.614417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile50027.8
Q1269253
median593103
Q3886791
95-th percentile1145480.8
Maximum1201086
Range1201075
Interquartile range (IQR)617538

Descriptive statistics

Standard deviation353935.3576
Coefficient of variation (CV)0.5989248289
Kurtosis-1.206751059
Mean590951.2188
Median Absolute Deviation (MAD)309342
Skewness0.01866636659
Sum818467438
Variance1.252702374 × 1011
MonotonicityNot monotonic
2022-02-01T17:23:18.815166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231792
 
0.1%
11345931
 
0.1%
3208501
 
0.1%
1426691
 
0.1%
4980361
 
0.1%
10847451
 
0.1%
6676311
 
0.1%
2035711
 
0.1%
1631421
 
0.1%
9639081
 
0.1%
Other values (1374)1374
99.2%
ValueCountFrequency (%)
111
0.1%
3851
0.1%
10091
0.1%
15001
0.1%
42101
0.1%
55741
0.1%
66771
0.1%
83311
0.1%
89841
0.1%
106421
0.1%
ValueCountFrequency (%)
12010861
0.1%
12007621
0.1%
11999011
0.1%
11990511
0.1%
11983101
0.1%
11975111
0.1%
11949361
0.1%
11943011
0.1%
11932001
0.1%
11923641
0.1%

RNA 4
Real number (ℝ≥0)

Distinct1384
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean600895.6498
Minimum5
Maximum1201715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:19.039488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile52951.8
Q1270893
median597869
Q3909093
95-th percentile1159783.4
Maximum1201715
Range1201710
Interquartile range (IQR)638200

Descriptive statistics

Standard deviation362315.1328
Coefficient of variation (CV)0.6029584885
Kurtosis-1.262065692
Mean600895.6498
Median Absolute Deviation (MAD)317201
Skewness0.004409373266
Sum832240475
Variance1.312722554 × 1011
MonotonicityNot monotonic
2022-02-01T17:23:19.260199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7093482
 
0.1%
7946251
 
0.1%
1263111
 
0.1%
2758271
 
0.1%
3230191
 
0.1%
5502571
 
0.1%
8754781
 
0.1%
11216461
 
0.1%
3642111
 
0.1%
11277871
 
0.1%
Other values (1374)1374
99.2%
ValueCountFrequency (%)
51
0.1%
1901
0.1%
7741
0.1%
8131
0.1%
15731
0.1%
35561
0.1%
37581
0.1%
38051
0.1%
38751
0.1%
47941
0.1%
ValueCountFrequency (%)
12017151
0.1%
12011231
0.1%
12006491
0.1%
12002731
0.1%
12002011
0.1%
12001941
0.1%
12001181
0.1%
11995131
0.1%
11993661
0.1%
11993481
0.1%

RNA 12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1001
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288753.6123
Minimum5
Maximum3731527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:19.655426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median234359
Q3524819
95-th percentile755950.6
Maximum3731527
Range3731522
Interquartile range (IQR)524814

Descriptive statistics

Standard deviation285350.6745
Coefficient of variation (CV)0.9882150816
Kurtosis13.75077862
Mean288753.6123
Median Absolute Deviation (MAD)234354
Skewness1.597587289
Sum399923753
Variance8.142500744 × 1010
MonotonicityNot monotonic
2022-02-01T17:23:19.836479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5385
 
27.8%
1487941
 
0.1%
7099091
 
0.1%
2265831
 
0.1%
2347811
 
0.1%
7406391
 
0.1%
668491
 
0.1%
4027231
 
0.1%
7549871
 
0.1%
1078201
 
0.1%
Other values (991)991
71.6%
ValueCountFrequency (%)
5385
27.8%
6171
 
0.1%
22061
 
0.1%
25171
 
0.1%
27441
 
0.1%
28981
 
0.1%
31431
 
0.1%
38531
 
0.1%
42301
 
0.1%
48541
 
0.1%
ValueCountFrequency (%)
37315271
0.1%
8100281
0.1%
8092451
0.1%
8066511
0.1%
8061091
0.1%
8056361
0.1%
8052181
0.1%
8036851
0.1%
8025171
0.1%
8019811
0.1%

RNA EOT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1002
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287660.3365
Minimum5
Maximum808450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:20.070684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median251376
Q3517806
95-th percentile747021.4
Maximum808450
Range808445
Interquartile range (IQR)517801

Descriptive statistics

Standard deviation264559.5251
Coefficient of variation (CV)0.9196941376
Kurtosis-1.202324311
Mean287660.3365
Median Absolute Deviation (MAD)251371
Skewness0.4115446543
Sum398409566
Variance6.999174231 × 1010
MonotonicityNot monotonic
2022-02-01T17:23:20.261677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5384
 
27.7%
1269761
 
0.1%
3863841
 
0.1%
4457421
 
0.1%
2151481
 
0.1%
771041
 
0.1%
2253521
 
0.1%
4723701
 
0.1%
218111
 
0.1%
5611521
 
0.1%
Other values (992)992
71.6%
ValueCountFrequency (%)
5384
27.7%
4081
 
0.1%
24601
 
0.1%
61981
 
0.1%
63251
 
0.1%
66361
 
0.1%
71151
 
0.1%
81651
 
0.1%
81961
 
0.1%
83901
 
0.1%
ValueCountFrequency (%)
8084501
0.1%
8081421
0.1%
8072181
0.1%
8071751
0.1%
8066691
0.1%
8066051
0.1%
8060281
0.1%
8057171
0.1%
8047701
0.1%
8047351
0.1%

RNA EF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1004
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291378.291
Minimum5
Maximum810333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:20.488360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median244049
Q3527864
95-th percentile753952.8
Maximum810333
Range810328
Interquartile range (IQR)527859

Descriptive statistics

Standard deviation267700.6917
Coefficient of variation (CV)0.9187393159
Kurtosis-1.230612399
Mean291378.291
Median Absolute Deviation (MAD)244044
Skewness0.4050748116
Sum403558933
Variance7.166366034 × 1010
MonotonicityNot monotonic
2022-02-01T17:23:20.698517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5382
 
27.6%
1966091
 
0.1%
4035571
 
0.1%
4888111
 
0.1%
1923691
 
0.1%
1386051
 
0.1%
1513961
 
0.1%
5236341
 
0.1%
1980051
 
0.1%
5011101
 
0.1%
Other values (994)994
71.8%
ValueCountFrequency (%)
5382
27.6%
5701
 
0.1%
14171
 
0.1%
18761
 
0.1%
25491
 
0.1%
26331
 
0.1%
28311
 
0.1%
50051
 
0.1%
56281
 
0.1%
65831
 
0.1%
ValueCountFrequency (%)
8103331
0.1%
8100561
0.1%
8090171
0.1%
8076601
0.1%
8076031
0.1%
8070761
0.1%
8064251
0.1%
8062041
0.1%
8050611
0.1%
8040361
0.1%

Baseline histological Grading
Real number (ℝ≥0)

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.761732852
Minimum3
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2022-02-01T17:23:20.885988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q16
median10
Q313
95-th percentile16
Maximum16
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.023896452
Coefficient of variation (CV)0.412211286
Kurtosis-1.220059766
Mean9.761732852
Median Absolute Deviation (MAD)4
Skewness-0.1059609037
Sum13520
Variance16.19174266
MonotonicityNot monotonic
2022-02-01T17:23:21.040246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
15139
10.0%
11121
 
8.7%
14106
 
7.7%
9103
 
7.4%
6102
 
7.4%
12101
 
7.3%
897
 
7.0%
1395
 
6.9%
494
 
6.8%
593
 
6.7%
Other values (4)334
24.1%
ValueCountFrequency (%)
389
6.4%
494
6.8%
593
6.7%
6102
7.4%
772
5.2%
897
7.0%
9103
7.4%
1087
6.3%
11121
8.7%
12101
7.3%
ValueCountFrequency (%)
1686
6.2%
15139
10.0%
14106
7.7%
1395
6.9%
12101
7.3%
11121
8.7%
1087
6.3%
9103
7.4%
897
7.0%
772
5.2%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
4
362 
3
355 
1
336 
2
332 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row4
4th row3
5th row1

Common Values

ValueCountFrequency (%)
4362
26.1%
3355
25.6%
1336
24.3%
2332
24.0%

Length

2022-02-01T17:23:21.211240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:21.308255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4362
26.1%
3355
25.6%
1336
24.3%
2332
24.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-02-01T17:23:06.720281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:08.226801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:11.561481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:14.733214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:17.760792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:20.672945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:23.592011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:26.521818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:29.341735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:32.166690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:34.827086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:37.587116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:41.077295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:44.466784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:47.921686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:51.176356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:54.005869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:57.216495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:00.355248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:03.469376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:06.857945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:08.465190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:11.701649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:14.864431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:17.900203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:21.018754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:23.730560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:26.666110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:29.471601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:32.294029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:35.113800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:37.713336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:41.303299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:44.593246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-01T17:22:51.312805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-01T17:22:57.542878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-01T17:23:03.770261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:07.119792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:08.790950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:11.955005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:15.126124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:18.215780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-01T17:22:44.866833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-01T17:22:51.580640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:54.593241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:57.669387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:00.814587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:03.924845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:07.261331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:08.943927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:12.105066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-01T17:22:18.388912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-01T17:22:24.109936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:27.054710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:29.860394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:32.685079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-01T17:22:27.180537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:29.966024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:32.822736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:35.605695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:38.326045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:41.988604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:45.209785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:48.742463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:51.853776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:54.862139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:57.993612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:01.099755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:04.235209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:07.546609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:09.258475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:12.395277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:15.542095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:18.691311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:21.711370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:24.579578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:27.310771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:30.119328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:32.955622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:35.735417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:38.479386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:42.165587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:45.366796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:48.902471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:52.011349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:55.008596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:58.138284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:01.253025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:04.435435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:07.659282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:09.565870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:12.541205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:15.658960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:18.817500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:21.835863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:24.705667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:27.431444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-01T17:22:34.372382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:37.175160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:40.510210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:43.971062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:47.398554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:50.494150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:53.574876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:56.682150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:59.880897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:03.007331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:06.279673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:09.339210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:11.234267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:14.371825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:17.446778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:20.380194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:23.312213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:26.231197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:29.062845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:31.865775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:34.526687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:37.325266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:40.699925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:44.160072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:47.554726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:50.658030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:53.711784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:56.885840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:00.052688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:03.167918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:06.414078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:09.485820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:11.416238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:14.578576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:17.617411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:20.525104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:23.447331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:26.372349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:29.206946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:32.003047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:34.698506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:37.460927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:40.888185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:44.326120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:47.741997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:51.032496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:53.865531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:22:57.054836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:00.218907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:03.318665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T17:23:06.575129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-01T17:23:21.480241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-01T17:23:21.838408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-01T17:23:22.236838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-01T17:23:22.666833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-01T17:23:23.135906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-01T17:23:09.790441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-01T17:23:10.889979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AgeGenderBMIFeverNausea/VomtingHeadacheDiarrheaFatigue & generalized bone acheJaundiceEpigastric painWBCRBCHGBPlatAST 1ALT 1ALT4ALT 12ALT 24ALT 36ALT 48ALT after 24 wRNA BaseRNA 4RNA 12RNA EOTRNA EFBaseline histological GradingBaselinehistological staging
056135211122274254248807.014112132.0998452.01098155565533063453628819455132
1461291221221121014429425.010129367.09112395.0751135712344406205386356370563368043108542
257133222211141784621191.012151522.01134995.0107116555571148661346573594555882944
349233121212164904794631.010146457.04364109.080884877331041941449939585688744463582301103
459132112122236614606375.011187684.09910467.0481209490306604107387563731527338946242861111
5582222221221117853882456.015131228.066104121.0966573114291157452108685255544
6422261122222116204747333.012177261.07857113.01181078480283256941034008275095214566635157124
748230112211273354405941.011216176.011911280.01274596533964112972050787295370605506296123
8441231122212104804608464.012148889.0938355.0102971223945591441757361537109020304252
945130212211266814455329.01298200.0556872.0127811254330115120623048826732027529555551642

Last rows

AgeGenderBMIFeverNausea/VomtingHeadacheDiarrheaFatigue & generalized bone acheJaundiceEpigastric painWBCRBCHGBPlatAST 1ALT 1ALT4ALT 12ALT 24ALT 36ALT 48ALT after 24 wRNA BaseRNA 4RNA 12RNA EOTRNA EFBaseline histological GradingBaselinehistological staging
137559128121122198674367201.013210688.08687102.0531191246525104623527089380665168337348445162
137646127212212136374453055.012120777.0404977.07310358120241680441027366555112
137752225121111174803922244.014183040.01095098.074506412241174686434026451156634462396750162
137833124111222177304627740.011180703.04212467.0905646127321174355103985353164072011813809164
137953131221222141964076324.012150065.08911352.0395486783688665646008059104062101420645134
138044129122211170444957174.015202520.01225978.010612763444538779555938555154
138155134122111162074636951.010115776.012810265.09910897644148137815296139333973574236273102
138242126221112149134122038.014128354.06193123.06111687392461266457275680610934371916045762
138352129211222172574241990.010205908.07097104.07447488143139872761615157302460696074153
1384552261222121118324059176.014136615.05112639.0681156471341190577628730555133